Recursive Motion Prediction Recursion Count Optimization
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Solution Overview
Problem
Existing object prediction technologies face challenges in managing the calculation amount, as recursive prediction processing can lead to either insufficient information for motion recognition with too few recursions or excessive calculation with too many recursions, making it difficult to accurately predict the motion of objects.
Innovation Solution
An information processing method that acquires and processes time-series images to predict the position or size of an object, recursively executes prediction processing based on the acquired information, and determines the number of recursions needed for accurate motion recognition, adjusting according to the recognized motion, type, direction, or speed of the object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recursive prediction processing is executed multiple times to improve motion recognition accuracy, then the recognition precision is improved, but the calculation amount increases excessively
Solution Approach 1:
The patent applies dynamics by making the number of recursive prediction processing executions variable rather than fixed. The system dynamically adjusts the recursion count based on the specific motion characteristics of each detected object, such as motion speed and trajectory complexity. This allows the system to execute more recursions for objects requiring higher prediction accuracy while using fewer recursions for simpler cases, thereby resolving the contradiction between recognition precision and calculation amount.
2Quantity of substance
If recursive prediction processing is executed fewer times to reduce calculation amount, then the computational burden is reduced, but the motion recognition accuracy becomes insufficient
Solution Approach 1:
The patent applies parameter changes by adjusting the number of recursive prediction processing executions based on motion parameters of the object. The system evaluates parameters such as motion speed, acceleration, and trajectory complexity to determine the optimal recursion count. This parameter-based adjustment ensures sufficient prediction accuracy for complex motions while avoiding unnecessary calculations for simple motions, thus resolving the contradiction between calculation amount and recognition accuracy.
3Ease of operation
If a fixed number of recursive predictions is used, then the processing is simple, but it cannot adapt to different motion characteristics of objects
Solution Approach 1:
The patent transforms the static, fixed recursion count into a dynamic parameter that adapts to different motion characteristics. The system incorporates motion analysis that evaluates object-specific parameters such as speed, acceleration, and trajectory patterns, then adjusts the recursion count accordingly. This dynamic approach maintains processing simplicity through automated decision-making while achieving high adaptability to various motion types.
Solution Approach 2:
The patent implements feedback mechanisms where the results of preliminary motion analysis are fed back into the decision-making process for determining the recursion count. The system evaluates motion characteristics, uses this feedback to determine the appropriate number of recursive predictions, and adjusts processing accordingly. This feedback loop enables the system to automatically adapt to different motion characteristics without complex manual configuration.
Data Source
AI summary
An information processing method includes processing of: acquiring, from a plurality of time-series images in which an object is captured, first information including at least a plurality of positions or a plurality of sizes of the object; executing prediction processing of predicting second information including at least one of a position or a size of the object at a next time point in a time-series based on the first information and recursively executing the prediction processing based on the first information and the second information to predict the second information of the object at a time point further next to the next time point; executing recognition processing of recognizing motion of the object based on the second information; and determining a total number of times of recursion of the prediction processing based on a result of the recognition processing.